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This project is a Python speech recognition library that serves as a unified interface for converting spoken audio into text. It functions as a bridge between Python applications and a variety of speech-to-text engines, providing a consistent way to interact with both local and cloud-based recognition services.
The main features of uberi/speech_recognition are: Speech-to-Text Integrations, Speech-to-Text Libraries, Long Audio Chunk Transcribers, Unified Provider Interfaces, On-Device Speech Recognizers, Speech Recognition Libraries, Speech Recognition APIs, Unified Recognition Interfaces.
Projects with overlapping indexed features include: k2-fsa/sherpa-onnx — Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device… soniqo/speech-swift — This project is a comprehensive toolkit for on-device speech recognition, synthesis, and audio processing,… jamsch/expo-speech-recognition — Expo Speech Recognition is a cross-platform mobile module that converts live microphone audio and pre-recorded files… sevask/ecoute — Ecoute is a live transcription tool that provides real-time transcripts for both the user's microphone input (You) and… syedhali/ezaudio — EZAudio is an audio library for Apple platforms that provides standardized interfaces for microphone capture, file… nl8590687/asrt_speechrecognition — This project is a Chinese automatic speech recognition framework and deep learning system designed to convert spoken…
Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device voice synthesis tool, and a speaker identification framework. It is designed as a cross-platform speech API that enables speech-to-text, text-to-speech, and speaker verification tasks to be executed locally on a device without requiring network access. The project is distinguished by its ability to perform zero-shot voice cloning and speaker diarization on-device. It supports a wide range of hardware accelerations, including GPU and various NPU architectures, and provides a Web
This project is a comprehensive toolkit for on-device speech recognition, synthesis, and audio processing, specifically engineered for Apple Silicon. It provides a framework for building real-time, full-duplex voice agents that operate entirely offline, leveraging native hardware acceleration to maintain performance and privacy. By utilizing optimized machine learning models, the library enables local execution of complex audio tasks without reliance on external cloud services. The library distinguishes itself through its specialized focus on local, high-performance voice interaction. It incl
Expo Speech Recognition is a cross-platform mobile module that converts live microphone audio and pre-recorded files into text using native speech engines. It provides offline speech recognition capabilities by downloading and verifying local speech models to enable on-device processing without an active network connection. The library includes session lifecycle management to start, stop, or abort recording, alongside real-time spoken language detection with confidence scoring. It emits volume change events for metering interfaces, handles audio session configuration and routing, and persist
Ecoute is a live transcription tool that provides real-time transcripts for both the user's microphone input (You) and the user's speakers output (Speaker) in a textbox.